Congenital heart disease (CHD) is the most common birth defect and affects 1% of all live born infants, and genetics likely contribute to 90%. While ~90% of patients with CHD survive into adulthood, there are many comorbidities that make CHD an increasingly significant public health problem. During the first two project periods the PCGC has made significant progress in understanding the genetic architecture of CHD. The highlights of the genomics work are the identification of de-novo variants contributing to ~10% of CHD, the identification of chromatin remodeling genes contributing to 2.3% of all CHD and to a striking 28% of CHD that is associated with extracardiac and neurodevelopmental abnormalities, the identification of novel CHD genes underlying inherited CHD, and the contribution of genes involved in cilia structure and function to CHD. The other most significant finding from the PCGC genomic studies is the tremendous heterogeneity of CHD: over 440 genes contribute to CHD by a dominant inheritance mechanism alone. Combining these findings with previous data on the contribution of copy-number variants and aneuploidy identifies a likely genetic cause for ~40% of CHD. Our hypotheses are that many yet unidentified mutations affecting the genic region contribute to a significant portion of CHD, and that the specific mutations contributing to CHD impact outcomes. The two major questions addressed in this proposal are what is the genetic contribution to the ?missing 55%?, and how do specific mutations impact the cardiac and non-cardiac outcomes and clinical care of patients with CHD? In the setting of large genetic heterogeneity and variable phenotypic expressivity that characterizes CHD, answers to these questions will require very large patient cohorts with genotype and phenotype data. Aim 1 will define the genetic architecture of CHD through analysis of 30,000 enrolled patients with genomic data in the combined PCGC cohort. Patients are recruited by outreach to the entire Pediatric Cardiology community along with internet-based direct patient recruiting. This will be coupled with a cost-effective tiered sequencing strategy that starts with MIPs-based targeted sequencing on all probands and progresses to whole-exome and whole- genome sequencing in MIPs-negative patients. Since the eventual goal of the PCGC program is to use genomic data to improve clinical care of CHD patients, we will need to link genomic and outcome data efficiently. Aim 2 will establish a central PCGC data mining center to directly link phenotypic data from the EMR and STS database with genomic data. We will initially focus on two outcomes that are readily available in the EMR and are associated with significant morbidity in CHD patients: the potential role of cilia mutations in progressive valve dysfunction in single-ventricle patients and the potential role of chromatin modifier gene mutations to cancer risk in adult CHD survivors. The informatics paradigms developed for this project can then be applied to investigate potential genetic contribution to a wide range of other CHD outcomes.